U.S. patent application number 10/836769 was filed with the patent office on 2005-11-03 for substance detection system.
Invention is credited to Uluyol, Onder.
Application Number | 20050244978 10/836769 |
Document ID | / |
Family ID | 35187615 |
Filed Date | 2005-11-03 |
United States Patent
Application |
20050244978 |
Kind Code |
A1 |
Uluyol, Onder |
November 3, 2005 |
Substance detection system
Abstract
A substance detection system having an electronic nose and
classifier. The system may extract feature from the electronic
signals representing the smells from the electronic nose sensing an
unknown substance. The features may be formatted as smellprints
that are synthesized as data. The features may be classified, and
in a binary tournament fashion, as an illustrative example, may be
mapped to be compared or correlated with features of known
substances. The known substance or substances having the highest
scores for good mapping, comparison or correlation with the sensed
substance, may be reviewed in view of decision criteria to
determine an identification of the sensed substance.
Inventors: |
Uluyol, Onder; (Fridley,
MN) |
Correspondence
Address: |
HONEYWELL INTERNATIONAL INC.
101 COLUMBIA ROAD
P O BOX 2245
MORRISTOWN
NJ
07962-2245
US
|
Family ID: |
35187615 |
Appl. No.: |
10/836769 |
Filed: |
April 30, 2004 |
Current U.S.
Class: |
436/149 ;
422/83 |
Current CPC
Class: |
G01N 33/0034
20130101 |
Class at
Publication: |
436/149 ;
422/083 |
International
Class: |
G01N 027/00 |
Claims
What is claimed is:
1. A substance detection system comprising: an electronic nose; a
feature extraction module connected to the electronic nose; a data
synthesis module connected to the feature extraction module; a
classification module connected to the data synthesis module; and a
decision criteria module connected to the classification
module.
2. The system of claim 1, wherein the classification module
comprises a support vector machine.
3. The system of claim 2, wherein the feature extraction module
provides a smellprint to the data synthesis module.
4. The system of claim 3, wherein the smellprint comprises a final
activation and initial response data.
5. The system of claim 4, wherein the output classification module
has an output for classification results.
6. The system of claim 5, wherein the decision criteria module is
for filtering the classification results.
7. The system of claim 6, wherein a first criterion for filtering
the classification results is a threshold.
8. The system of claim 6, wherein a criterion for filtering the
classification results is a winner-take-all standard.
9. The system of claim 6, wherein the feature extraction module
comprises: a baseline flag; a baseline resistance processor having
a first input from the electronic nose and a second input from the
baseline flag; a fractional normalizer having a first input from
the electronic nose and a second input from the baseline
resistance; a filter having an input connected to the fractional
normalizer; a gradient indicator having an input connected to the
filter; a steady-state period indicator at the baseline purge
having an input connected to the baseline flag; a sample draw flag;
a transient period indicator at sample draw having an input
connected to the sample draw flag; an indicator of average or
absolute variation at baseline having a first input connected to
the steady-state period indicator at baseline purge and a second
input connected to the gradient indicator; an indicator of maximum
absolute variation at initial response having a first input
connected to the transient period indicator at sample draw and
having a second input connected to the gradient indicator; a
relative variation processor having a first input connected to the
indicator of average of absolute variation at baseline and a second
input connected to the indicator of maximum absolute variation at
initial response; a vector normalizer having an input connected to
the relative variation processor; and an initial response feature
vector indicator having an input connected to the vector normalizer
and an output connected to the data synthesis module.
10. The system of claim 6, further comprising a leave-one-out
connection from the classification module to the data synthesis
module.
11. A method for detecting a substance comprising: sensing a
substance with an electronic nose providing a signal corresponding
to the substance; extracting at least one feature from the signal;
synthesizing the at least one feature; classifying the at least one
feature; associating the at least one feature with at least one
feature of a set of known substances; selecting a substance of the
set that most likely resembles the sensed substance according to
the at least one feature; and identifying the sensed substance.
12. The method of claim 11, wherein: the at least one feature is a
first smellprint; and the classifying the at least one feature
involves a support vector machine.
13. The method of claim 12, further comprising: selecting one of
each pair of substance smellprints of a plurality of smellprints of
substances that compares most closely to the first smellprint; and
determining which smellprint of the plurality of smellprints of
substances is most selected.
14. The method of claim 13, further comprising: applying a decision
criterion relative to the smellprint that is most selected; and
determining whether the most selected smellprint is the same as the
first smellprint so as to identify the substance sensed by the
electronic nose.
15. Means for detecting a substance comprising: means for sensing a
substance via a smell; means for converting the smell into at least
one signal; means for extracting features from the at least one
signal; means for mapping features to features of each of a
plurality of known substances; means for indicating a score of
mapping for each of the known substances; means for selecting the
known substance having the highest score; and means for determining
whether the sensed substance is identified as the same as the known
substance having the highest score.
16. The means of claim 15, wherein the means for determining
comprises a criterion selected from a group of the threshold levels
and winner-take-all criteria.
17. The means of claim 16, wherein the mapping of features is a
binary tournament classification.
18. The means of claim 17, wherein the binary tournament
classification comprises a support vector machine.
19. A substance detection system comprising: an electronic smelling
mechanism having a signal output representing a detected smell; a
feature extraction device connected to the electronic smelling
mechanism; and a feature classification module, connected to the
feature extraction device, having an output indicating at least one
known substance that has a smell similar to the detected smell.
20. The system of claim 19, further comprising a decision maker
connected to the output of the feature classification module.
21. The system of claim 19, wherein the decision maker contains
criteria relating to whether the detected smell is from a known
substance.
22. The system of claim 21, wherein the feature classification
module comprises a support vector machine.
Description
BACKGROUND
[0001] The present invention pertains to sensing and particularly
to the detection of substances. More particularly, the invention
pertains to classification of detected substances.
[0002] There appears to be a need for a portable substance
detection system, notably for detecting fluids, which may include
an electronic nose or smell sensor having good selectivity and
sensitivity.
SUMMARY
[0003] The invention provides an approach for robust detection of
analytes using composite sensors while increasing selectivity and
sensitivity. An illustrative example may involve several
complementary parts. The one part may be to extract class-dependent
information from the sensor's initial response at a sample draw.
Another part may be to advance the discriminatory tools applied on
raw data by developing high margin classifiers. The term
"substance" in the present patent document may be understood and
used in an inclusive and broad sense to mean a fluid, compound,
analyte, particle, material, or other matter that may be present in
chemical vapor phase.
BRIEF DESCRIPTION OF THE DRAWING
[0004] FIGS. 1-5 show baseline and sample draw signal diagrams from
an electronic nose proximate to five different known
substances;
[0005] FIGS. 6-10 are smellprints of the five known substances;
[0006] FIGS. 11-18 show tables of detection results relative to
various classifications and decision criteria;
[0007] FIG. 19 shows an illustrative example of a substance
detection system;
[0008] FIG. 20 is a diagram of a feature extraction mechanism;
and
[0009] FIG. 21 shows a test set-up for a substance detection and
identification system.
DESCRIPTION
[0010] The invention, i.e., the substance detection and
classification system, may include generating a smellprint which is
based on initial reactions of the chemiresistors rather than the
bulk relative resistance change. The invention may also present a
robust classification approach employing a support vector machine
(SVM) process. Various combinations of smellprints--including their
projections to a small number of principal components--may be
analyzed. Binary support vector machine classification results may
be filtered through several different mechanisms: a set threshold
on the total vote, and a winner-take-all method. The classification
accuracy may be determined through the leave-one-out (LOO)
procedure.
[0011] The system may provide a smellprint of a substance derived
from polymer-based electronic nose, and a robust classification
method. The system may be used for identifying five, more or less,
compounds. The system could also be used to detect the presence of
one compound. Results may be obtained in terms of improved
detection at low concentrations and reduced false alarm rates.
[0012] For portable substance detection systems employed as
electronic noses, selectivity and sensitivity remain as primary
features attractive for their wide acceptance and use. Selectivity
refers to the system's ability to detect the presence of an analyte
of interest over the detection of what is not of interest. This
property may be tuned in the design and manufacturing process of
the sensor to a certain degree. However, the tuning then may limit
the possible areas of application of the sensor.
[0013] The definition of sensitivity varies depending on the task.
If the task is classification, sensitivity may refer to the
detection threshold of a system in terms of chemical concentration.
If however the task is regression, then sensitivity may refer to
the system's ability to measure the smallest increment in chemical
concentration, or partial pressure.
[0014] In recent years, there has been extensive research conducted
in optimizing the physical properties of substance detection
systems to achieve robustness by improving selectivity and
sensitivity characteristics. The approaches vary from doping the
sensors with various metals to increasing precision in grain or
pellet sizes. In addition, the successes in miniaturization have
led to the development of sensor arrays. Sensor arrays may permit
increased selectivity by including more sensors that are tuned for
different analytes. Redundancy in sensor arrays may also help in
increasing the signal to noise ratio. However, in conducting
polymers this advantage is masked by the increased noise in DC
measurements of the resistance.
[0015] Another focus may be on data analysis and algorithm
development that lead to improvements in selectivity and
sensitivity. The approach may be demonstrated on a polymer-based
electronic nose sensor for detecting five chemicals used in
aerospace applications, such as gas turbine oil, hydraulic fluid,
two types of deicing fluids, and jet fuel.
[0016] The system's approach for robust classification while
increasing sensitivity may be two fold. The first is to extract
class-dependent information from the sensors in addition to the
one-dimensional "smellprint" which may be provided by an
off-the-shelf product. The second is to advance the discriminatory
tools applied on raw data with classification algorithms.
[0017] "Smell-detectors" or "electronic noses" are substance
detecting devices based on a technique of full expansion which may
use the absorption by polymeric membranes of analytes present in
fluids or other substances. The absorption of the analytes by the
polymeric membranes may generate an alteration of its physical
properties, such as density, thickness, refractive index,
resistivity, and the like.
[0018] Typically, an electronic nose or smell-detector may be
composed of many sensors made from different polymer compositions,
each having its own reaction to the presence of a given substance.
Electronic noses or smell-detectors generally may measure the
change in resistivity of the polymer membranes. However, since
polymers are rarely conductive, it may be necessary to mix
conductive particles, for example, carbon-black, to the polymeric
material for increasing the conductivity of the membrane.
[0019] The electronic nose or smell-detector may consist of an
array of polymer films embedded with conductive or resistive
material. When exposed to substance such as a fluid, e.g., a gas,
as applied to the present system, the polymer films may swell or
contract thereby leading to a change in the DC electrical
resistance of the film. The DC resistance across each of the films
in the array may be sampled at approximately uniformly-spaced times
resulting in a smellprint. The baseline sample draw as well as the
purge periods may be marked.
[0020] An example of an off-the-shelf electronic nose may be a
Cyranose.RTM. 320, available from Cyrano Sciences Inc., which is a
device that may use polymer composite sensors that swell or
contract when exposed to, for example, a vapor-phase analyte. It
may include an array of 32 sensors, each of which consists of a
pair of electrical contacts that are bridged by a composite film.
Typically the film for each sensor may be made of a composite of a
non-conducting polymer and conductive carbon black particles. When
the film absorbs vapor analytes and swells, the conductive pathways
in the film are broken and the resistance of the composite film
changes. The change in resistance between the electrical contacts
may be used as the output of the sensor. The film may be regarded
as a chemiresistor. Since each sensor in the array contains a
unique polymer, there may be a reproducible combination of
resistances or a smellprint for each vapor mixture. Polymers with a
range of properties can be chosen so that the sensor array may be
used to distinguish many different types of substances. The
responses from the chemiresistors may then be measured as a bulk
relative resistance change (R.sub.max/R.sub.baseline), and be used
to form a smellprint. An electronic nose having a number of sensors
other than 32 may be used.
[0021] The 32-element or other number-element smellprint vector of
the electronic nose may then be used for detecting, for instance,
certain chemicals. There may be two factors relative to this
approach. First, the final activation values may be dependent on
the duration of sample draw, and second, the variance of final
activation values over many exposures may be notable. These
characteristics may cause chemical detection and repeatability
issues. To alleviate these characteristics, additional features may
be extracted from sensor signals as an alternative to, or in
addition to, the original smellprint.
[0022] The initial rather than the final sensor response signal may
be used as the smellprint. The signal preprocessing and feature
extraction algorithms of the detection system that are developed
may allow capturing the initial change of resistivity at the
beginning of the sample draw period which provides unique and
consistent signatures for different chemicals. In addition, the
smellprint formed with initial sensor responses does not
necessarily suffer from the problems mentioned for the final
activation case. This additional smellprint may become very
important especially at very low headspace concentrations where the
bulk relative resistance change is either very low--sometimes due
to short sample draw duration--or not consistent.
[0023] The chemical signatures formed based on the initial sensor
responses may be unique and consistent. The results show that the
classification performance achieved using only the initial response
signatures may be comparable to or better than the one obtained
with the original smellprint. When the two sets are combined,
significant improvements in classification performance may be
noted. For classification, the method of support vector machine may
be employed. This kernel-based technique is shown to be more
powerful and robust than the ones included with other electronic
noses such as the Cyranose.RTM. 320 system.
[0024] The data set used to develop and test the algorithms may be
obtained from various tests. Five compounds, as an illustrative set
of compounds, may be considered: Mobil Jet II.TM.--turbine oil,
Skydrol LD--4.TM.--hydraulic fluid, Octaflo.TM. and
Maxflight.TM.--deicing fluids, and Jet A.TM.--jet fuel. Eight
exposures for each compound may be analyzed. The typical signals
received from polymer sensors during the baseline and sample draw
periods are shown in FIGS. 1 through 5 for five different
compounds. Each line in each figure corresponds to a different
exposure of the same chemical. Notice that the signal amplitude is
very low for some chemicals such as Mobil Jet.TM. and Skydrol.TM..
Also note that the sensor response varies greatly from one exposure
to another.
[0025] FIGS. 1 through 5 show the baseline and sample draw signals
for the following compounds, Maxflight.TM., Jet A.TM., Mobil
Jet.TM., Skydrol.TM. and Octaflo.TM., respectively. The exposures
may be numbered 1-8 in each of the FIGS. 1-5. Any other number of
compounds and other quantities of exposures may be utilized. FIGS.
6 through 10 show smellprints 62, 63, 64, 65 and 66 formed based on
the initial changes in the resistivity of the electronic nose's
array of polymer composite sensors for the cases of Maxflight.TM.,
Jet.TM., Mobil Jet.TM., Skydrol.TM. and Octaflo.TM., respectively.
These figures illustrate smellprints based on the initial responses
of sensors 1-32 (i.e., sensor no.) of the electronic nose for the
respective substances. The combinations of magnitudes 61 of
normalized activation shown in smellprints may be unique to the
substance or compound detected by the electronic nose.
[0026] Support vector machines (SVM) are powerful tools for data
classification. Classification may be achieved by a linear or
nonlinear separating surface in the input space of the data set. A
subset of data may be used to form the set of support vectors which
define the separating surface.
[0027] Given an 1-by-1 kernel matrix K, a binary SVM classifier may
be trained for the following formulation: 1 minimize : 1 2 i = 1 l
j = 1 l i j y i y j K ( x i , x j ) - i = 1 l i subject to : 0 i C
, i = 1 l i y i = 0
[0028] where l is the number of training examples, y.sub.i is the
label (+1 for positive example, -1 for negative) for the i-th
training example (x.sub.i) and K(x.sub.i,x.sub.j) denotes the value
of the kernel function for the i-th and j-th examples of x.
[0029] The SVM may maximize the margin distance between the nearest
positive and negative examples (in kernel feature space), which has
been shown to lead to excellent generalization performance. The
tests may be conducted using two different smellprints, which
include the final activation data (FA smellprint), and the initial
response data (IR smellprint). Also included may be the results for
the combined case (FA+IR). The effect of using smellprints directly
in classification versus using a small number of principal
components may be useful. Support vector machine classification
results may be filtered through a set threshold and a
winner-take-all mechanism.
[0030] Because one may employ a binary classifier on a multiple
class problem, a set of classifiers is trained and the pair-wise
voting scheme may be used for final labeling of each case. In
pair-wise voting, k(k-1)/2 classifiers may be needed for each
pair-wise contest. In the present illustrative example, ten
classifiers may be built for five compounds, k=5.
[0031] The classification accuracy may be determined through the
leave-one-out (LOO) procedure. That is, the data from one exposure
for each compound may be withheld and the SVM be trained with the
remaining exposures. The resulting classifier may then be tested
with the data that it has not seen during the training.
[0032] Sensor values may be obtained with a detection threshold. In
this case, the FA and IR smellprints may be used directly in
training. The detection threshold may be set to 0.75. This means
that for a positive detection to be made, the pair-wise voting
result may be larger than or equal to 75 percent. The FA smellprint
may result in a very high ambiguous detection rate while the IR
data may provide much higher correct classification rate. The
combined classifier may give better results than either one alone,
the robustness may be improved.
[0033] The table of FIG. 11 shows the overall classification
results. The columns "C", "M", "A", and "N" indicate "Correct
Classification", "Misclassification", "Ambiguous Classification",
and "No Detection", respectively. The meanings of "Correct
Classification" and "Misclassification" are literal. "Ambiguous
Classification" means that one other compound besides the correct
one is voted high. "No Detection" means no compound received votes
higher than the threshold. The results are given over 40 exposures,
i.e., 8 exposures each for 5 compounds. The table of FIG. 11
reveals detection results with the detection threshold (theta)
equal to or greater than 0.75. The table of FIG. 12 details the
results per compound. One may note that although the overall
performance is poor, Jet A.TM. fuel is identified correctly 100
percent in all three cases. The combined classifier appears to
improve the results dramatically for the case of Maxflight.TM. as
well. However the remaining three compounds are not detected about
half the time. This table shows detection results for each compound
with theta =0.75.
[0034] Using sensor values with a winner-take-all approach, the FA
and IR smellprints may be used directly for classification again,
but this time the winner-take-all procedure may be applied to the
voting results. Hence the "N" column (No Detection) may be removed
from the relevant tables.
[0035] The results presented in the tables of FIG. 13 and 14 show
that both the overall accuracy and the individual compound
detection rate improve significantly in this case. However, while
the ambiguous classification is reduced, some of those cases may be
included in the misclassified cases column. The table of FIG. 13
shows detection results with the winner-take-all approach. The
table of FIG. 14 shows detection results for each compound with the
winner-take-all approach.
[0036] The principal components with a detection threshold may be
noted. It appears that if the same classification training
performance could be achieved using a smaller number of features, a
better generalization would be achieved. To this end, the 32
dimensional smellprint data may be processed for its principal
components. SVM may be retrained using only the first ten retained
principal components. The tables of FIGS. 15 and 16 show the
results for the case of using a threshold of 0.75 on the pair-wise
voting results. FIG. 15 shows detection results with principal
components and theta =0.75. FIG. 16 shows detection results for
each compound with principal components and theta =0.75.
[0037] The principal components with the winner-take-all approach
may be noted. This case shows the results of using 10 principal
components and the winner-take-all procedure. The best overall
results may be achieved in this case. 35 out of 40 cases are
correctly identified. The table of FIG. 17 shows detection results
with principal components and the winner-take-all approach. The
table of FIG. 18 shows the detection results for each compound with
principal components and winner-take-all.
[0038] Electronic nose sensitivity and selectivity may be noted.
Improvements on these areas include data preprocessing, feature
extraction, and a classification algorithm. Headspace data from
five compounds used in aerospace applications may be analyzed. A
new smellprint based on the initial reactions of the chemiresistors
in addition to the bulk relative resistance change may be computed
as a way to increased robustness. Also presented may be a
classification approach employing the support vector machine
process. Various combinations of the two smellprints-including
their projections to a small number of principal components may be
analyzed. The binary support vector machine classification results
may be filtered through two different mechanisms, which may include
a set threshold on the total vote, and a winner-take-all
method.
[0039] When a small number of exposures are available (8 for each
of the 5 compounds), the classification accuracy may be determined
through the leave-one-out (LOO) procedure. The best results may be
obtained when the binary support vector machine process is applied
on a small set of features obtained through principal component
analysis and the outcome may be determined through the
winner-take-all approach. The new smellprint may provide better
discriminatory information in most cases than the original
smellprint.
[0040] An approach may incorporate the deciding between two
possible candidates in comparison with a substance such as a fluid
to be identified. The comparison may be with another substance that
is similar for votes. A comparison of different substances may
result in few votes or a small score. One sample or test datum may
have features extracted to a SVM which is told of five possible
substances. All 5 substances are looked at and compared as pairs
with the sample, i.e., a binary comparison. The substance may be
matched to one of the two such as a test case to substance 1 or
substance 2. The test substance may be compared to the following
pairs and one of the two may be picked in each comparison and the
picked substance that gets most of the votes is the identifying
substance. The pairs may include 1 and 2, 1 and 3, 1 and 4, 1 and
5, 2 and 3, 2 and 4, 2 and 5, 3 and 4, 3 and 5, and 4 and 5. Each
number appears 4 times. The most selected number may have 4 votes
which is the maximum. The vote may be 3 or there may be a tie of 3
of each. The base number of the total of different substances being
paired off for comparison and selection may be other than 5.
[0041] From the electronic nose may be signals relative to a
substance being sensed. The change in the electrical resistance
across the polymer sensor as it is exposed to an analyte may
constitute the raw data signal. Two types of smellprints may be
extracted from the raw sensor data. An FA smellprint 14 (shown in
FIG. 19) may refer to the smellprint formed based on the final
activations of sensor array at the end of the sample draw. FA
smellprint 14 may be computed as the change in the resistivity
relative to the baseline resistivity, .DELTA.R/R.sub.baseline. An
IR smellprint 15 may be formed based on the initial response of the
sensor array to the presence of an analyte. The IR smellprint 15
may take advantage of the transient response characteristics of
polymer sensors. The sensor response within 6-10 samples following
the introduction of analyte may be observed to be distinct to each
analyte-sensor pair and consistent across many exposures. However,
using this data for detection may require a robust feature
extraction process of a module 13 (in FIG. 19) since the signatures
formed during the transient can be subtle and the signal-to-noise
ratio might be high.
[0042] FIG. 19 shows an analyte (e.g., a target substance in a
fluid) sensing and classification system 10. System 10 may be
utilized to identify a sensed substance. A sensor resistivity
signal 11 (31 in FIG. 20) and baseline / sample draw flags (33, 38
in FIG. 20) signal 12 may go to the feature extraction module 13
that may implement feature extraction. From module 13, FA
smellprint 14 and IR smellprint 15 may go a data synthesis module
16. The data synthesis may incorporate and process FA, IR and FA+IR
data plus the PCA (principal component analysis) for an output 17
to a binary tournament classification module 18. The synthesized
data 17 may go to a select classes sub-module 19. Data 20 processed
by sub-module 19 may go to a support vector machine 21. An output
22 from SVM 21 may go to a cumulative score sub-module 23 to be
tabulated. An output 24 from sub-module 23 may go to the select
classes module 19, thereby forming a feedback loop. There may be
several outputs from module 18. One output 25 may be a
leave-one-output (LOO) that goes to the data synthesis module 16.
Another output 26 from module 18 may include binary data
classification information that goes to a decision criteria module
27. The information or data output 26 may be processed by module 27
according to a threshold and/or winner-take-all criterion. An
output 28 from module 27 may identify the substance sensed by the
electronic nose.
[0043] FIG. 20 shows a module 30 with the steps, states,
sub-modules or stages involved in the computation of IR smellprint
15 feature vector. Module 30 may have numerous aspects of the
feature extraction module 13 of system 10 in FIG. 19. The raw
sensor data may be an electronic nose sensor resistivity signal 31
(11 in FIG. 19) that may be taken in the form of time series data.
The periods for baseline purge and sample draw may also be
available. Sub-module 38 may be a sample draw flag. As a first step
in the preprocessing the raw data 31, a baseline resistivity 32 may
be computed. Sub-module 32 may be a processor. The corresponding
portion of the time series data as marked by the baseline flag 33
may be used. The conventional approach of arithmetic averaging the
baseline data for computing the baseline resistance 32 may be prone
to failure, mainly because the baseline purge period 37 might
contain high resistance data especially when successive
measurements are taken with short purge periods in between. For
this reason, a more robust method may be sought. Sub-module 37 may
be a steady state period indicator at the baseline purge. In view
of this, using the median which is more robust to outliers may be
useful.
[0044] The fractional normalization 34 may be done by subtracting
and dividing the raw sensor data from state 31 with the baseline
resistance 32. Sub-module 34 may be a normalizer. This may generate
a dimensionless and normalized sensor data, and remove the effect
of the additive drift as well as the multiplicative drift that the
sensor may experience.
[0045] The filtering 35 may be done next to remove noise across
each exposure. Sub-module 35 may be a filter. The type of filter to
be used at this stage may depend on the length of the baseline
period and the length of the initial response region of interest. A
filter whose edge effects do not extend to the transient region and
the baseline region immediately preceding it may be useful. The
output of filtering 35 may go through a gradient state 36.
Sub-module 36 may be a gradient indicator.
[0046] The polymer sensors, when exposed to an analyte, swell or
shrink thereby causing a change in the resistance. The magnitude of
this change in a given time may vary depending on the
sensor-analyte pair. Therefore, a rate of change may be computed
next for each sample.
[0047] The baseline purge may bring the sensor volume to its
nominal size. This state 37 may include an initial transient
followed by a steady-state region. Once the sample draw is
initiated at state 38, the sensor response may go through a
transient period 39 followed by a steady-state 37 again--this time
in the opposite direction. Sub-module 39 may be a transient period
indicator at sample draw. For the IR smellprint 15, the average
variation 41 at the steady-state 37 of the baseline purge with the
gradient 36 output and the maximum variation 42 at the transient
period 39 of the sample draw with the gradient 36 output may be
used to compute a relative variation 43. This may be akin to
computing a z-score for the sensor. The absolute maximum 42 of the
relative variation 43 in the transient region 39 of sample draw may
then be taken as the feature value representing the initial
response of the sensor. Sub-module 43 may be a relative variation
processor. A typical steady-state length 37 used for the baseline
purge may be 10 samples and a typical length for the initial
transient 39 may be 6 samples. Sub-module 41 may be an indicator of
average of absolute variation at baseline. Sub-module 42 may be an
indicator of maximum absolute variation at initial response.
[0048] Finally, the feature vectors may be normalized to account
for variations across different exposures by dividing each vector
by its norm. This step or sub-module may be called a vector
normalization or normalizer 44. The IR smellprint 15 may then be
formed as a 32 element vector at stage or indicator sub-module
45--one element for each sensor in the gas sensor array. However,
more or less elements may be incorporated in the detection system
10.
[0049] FIG. 21 shows a test system set-up 40 for evaluating an
electronic nose 71 system. A mixture of air and fluid 72 may be
inserted into a mixing chamber 73 by a sprayer nozzle 74. The fluid
72 fed by syringe pump 75 from supply tank 46 may go to sprayer
nozzle 74. Also, air may be fed to nozzle 74 from a regulated air
supply 47 via mass air flow controller 48. Additional air (i.e.,
make-up air) may be added to the mixing chamber 73 from a regulated
air supply 49 via a mass air flow controller 51. On top of the
mixing chamber 73 may be a "smoke-stack" like exhaust port 52. An
inlet probe 53 of electronic nose 71 may be inserted into port 52
for detection of fluid 72. Electronic nose 71 may be connected to a
data acquisition computer 54. Computer 54 may process the signals
from nose 71 into signals that identify the fluid 72 particles.
Computer 54 may have a substance detection system like one
described in the present description. Also, a temperature sensor 55
may be situated in exhaust port 52 of chamber 73. A temperature
signal from sensor 55 may be sent to computer 54 to provide
temperature compensation for the substance detection system.
[0050] Although the invention has been described with respect to at
least one illustrative embodiment, many variations and
modifications will become apparent to those skilled in the art upon
reading the present specification. It is therefore the intention
that the appended claims be interpreted as broadly as possible in
view of the prior art to include all such variations and
modifications.
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